2nd Sem Graduation Project

Graduation Project Image Reviving

Welcome Prof. Sayed El- Harbete. Dr. Ahmed Gawish.

Apology For no Meetings: No Clear Methodologies. Midterm. Feast.

Project Phases Determining the geometric class of an image region through:- Determining Super Pixels. Segmentation(Constellations). Labeling. ,[object Object],Cutting and Folding. Determining Camera Parameters.

Project Phases Determining the geometric classes of an image region. What is it?

Geometric classes for the image Input Image Vertical Labels Support Sky Center Left Right

Geometric classes for the image(Cont’) How? Across ( Color, Texture, Location in the image, shape, and projective geometry cues(using vanishing line)). Steps :- Determining Super Pixels. Segmentation(Constellations). Geometric Classification (using the classifier). Classifier :- determine each segment belong to which class (ground, vertical (Left ,Right , Center) ,sky).

Project Phases(Cont’) Cutting and Folding (Texture mapping). Determining Camera Parameters and Determining the 3D model Coordinates.

Project Phases(Cont’) 10 Original Image Object representation due to the 2D image Objects representation due to 3D Computer Vision

[object Object],Initially, an image is represented simply by a 2D array of RGB pixels. Without any knowledge of how those pixels should be grouped, we can compute only simple cues, such as pixel colors and filter responses. Our first step is to form super pixels from those raw pixel intensities. Super pixels correspond to small, nearly-uniform regions in the image and have been found useful by other computer vision and graphics researchers

Determining the geometric classes for the image (Cont’) Segmentation(Forming the Constellations) Constellations Super pixels

Determining the geometric classes for the image (Cont’) After the first phase we will be able to get the labeled image ( Ground , Vertical , Sky). Labeled image

Segmentation Algo. Start 80 ______________________ Sigma = 20 ______________________ Accept region = [80-20 , 80+20] = [60 , 100] Queue Start Pixel 9: 3  12 8: 1  6 8: 1014 8: 1  6 10 : 12 8: 1014 8: 1  6 10 :714 10 : 23 8: 1014 8: 1  6 Dequeue (Old) ______________ Enqueue (New) Enqueue (New)

Challeges Result Problem1: (edges between the segments)

Solution for the edges Apply Smoothing Filters Original Smoothing 7

Challeges Result Problem2: (Start Position)

Solution for the Start Original Put the fill color by Mean : Problem Mean 2nd Segment

Challeges Result Problem3: (Small Region)

1st Solution for the small regions 1- Select Small region segment 2- Choose nearest Large region 3- Fill Small By Large No Applicable

2nd Solution for the small regions 1- Smooth Original . 2- Put the fill Segment by Mean Color . 3- Start with small Sigma to Large : Orginal Smooth Mean

Adv. From 3rd Problem To recognize between the Textures: Smooth Texture coarse Texture

2nd Sem Graduation Project

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